{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "display(Image(filename=image_path)) #local path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "DyF2YSMshMmA",
        "outputId": "b329bc95-1f16-4948-91e8-3e280c687276"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How to set up the Swarm (OpenAI) multi-agent framework for financial analysis?\n",
        "\n",
        "Swarm is a multi-agent orchestration framework developed by OpenAI. Currently, it serves as an experimental framework designed to explore user-friendly interfaces for multi-agent systems. It's not intended for production use and lacks official support.\n",
        "\n",
        "\n",
        "👉 Pros and Cons:\n",
        "\n",
        "-  **Pros**: Swarm is designed to be lightweight, scalable, and easy to customize. You can add and manage many independent tasks and instructions that are hard to fit into a single prompt. Each agent can be assigned unique instructions and functions, allowing it to specialize in specific tasks.\n",
        "\n",
        "\n",
        "-  **Cons**: Swarm requires manual memory management by the user, in contrast to the Assistant AI API, which provides fully hosted threads and integrated memory management.\n",
        "\n",
        "\n",
        "👉 So, How to set up the Swarm multi-agent framework for financial analysis purpose?\n",
        "\n",
        "\n",
        "I created 4 agents:\n",
        "\n",
        "-  **manager_agent**: This agent will handle determning and switching between agents to transfer the user's request to the best suited agent.\n",
        "\n",
        "-  **stock_price_agent**: This agent is responsible of fetching the last available price, volume, average price 50d, 200d, EPS, PE, and the next earnings annoucement.\n",
        "\n",
        "-  **company_basic_information_agent**: This agent is responsible of collecting the company basic data like description market capitalization, sector, industry...\n",
        "\n",
        "-  **income_statement_agent**: This agent is responsible of gathering the last income statement data such as revenue, gross profit, net income, EBITDA, EPS.\n",
        "\n",
        "* For each sub-agent, I define a set of functions to handle collecting the appropriate data.\n",
        "* The manager agent will manage functions that distribute the request to the various agents.\n",
        "* Each sub-agent will have a function to transfer request back to the manager.\n",
        "\n",
        "\n",
        "👉 Discover how this framework works in this notebook: 👇\n",
        "\n",
        "[Hanane DUPOUY](https://www.linkedin.com/in/hanane-d-algo-trader)"
      ],
      "metadata": {
        "id": "hrIanfhjdTaj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install Swarm"
      ],
      "metadata": {
        "id": "b14Q0u6CbDP6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MufH-jdRKdnk"
      },
      "outputs": [],
      "source": [
        "pip install git+https://github.com/openai/swarm.git -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n",
        "\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
        "\n",
        "import requests\n",
        "\n",
        "FINANCIAL_MODELING_PREP_API_KEY = userdata.get('FINANCIAL_MODELING_PREP_API_KEY')"
      ],
      "metadata": {
        "id": "bLAZezuoKscd"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define the functions:"
      ],
      "metadata": {
        "id": "ixXhU0pRbGaP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By using **Financial Modeling PREP API**, I created these 3 functions to fetch different financial data for a given company symbol:\n",
        "\n",
        "**get_stock_price**, **get_company_financials**, **get_income_statement**"
      ],
      "metadata": {
        "id": "omAESCusk5AA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_stock_price(symbol):\n",
        "    \"\"\"\n",
        "    Fetch the current stock price for the given symbol, the current volume, the average price 50d and 200d, EPS, PE and the next earnings Announcement.\n",
        "    \"\"\"\n",
        "    url = f\"https://financialmodelingprep.com/api/v3/quote-order/{symbol}?apikey={FINANCIAL_MODELING_PREP_API_KEY}\"\n",
        "    response = requests.get(url)\n",
        "    data = response.json()\n",
        "    try:\n",
        "        price = data[0]['price']\n",
        "        volume = data[0]['volume']\n",
        "        priceAvg50 = data[0]['priceAvg50']\n",
        "        priceAvg200 = data[0]['priceAvg200']\n",
        "        eps = data[0]['eps']\n",
        "        pe = data[0]['pe']\n",
        "        earningsAnnouncement = data[0]['earningsAnnouncement']\n",
        "        return {\"symbol\": symbol.upper(), \"price\": price, \"volume\":volume,\"priceAvg50\":priceAvg50, \"priceAvg200\":priceAvg200, \"EPS\":eps, \"PE\":pe, \"earningsAnnouncement\":earningsAnnouncement }\n",
        "    except (IndexError, KeyError):\n",
        "        return {\"error\": f\"Could not fetch price for symbol: {symbol}\"}\n",
        "\n",
        "def get_company_financials(symbol):\n",
        "    \"\"\"\n",
        "    Fetch basic financial information for the given company symbol such as the industry, the sector, the name of the company, and the market capitalization.\n",
        "    \"\"\"\n",
        "    url = f\"https://financialmodelingprep.com/api/v3/profile/{symbol}?apikey={FINANCIAL_MODELING_PREP_API_KEY}\"\n",
        "    response = requests.get(url)\n",
        "    data = response.json()\n",
        "    try:\n",
        "        results = data[0]\n",
        "        financials = {\n",
        "            \"symbol\": results[\"symbol\"],\n",
        "            \"companyName\": results[\"companyName\"],\n",
        "            \"marketCap\": results[\"mktCap\"],\n",
        "            \"industry\": results[\"industry\"],\n",
        "            \"sector\": results[\"sector\"],\n",
        "            \"website\": results[\"website\"],\n",
        "            \"beta\":results[\"beta\"],\n",
        "            \"price\":results[\"price\"],\n",
        "        }\n",
        "        return financials\n",
        "    except (IndexError, KeyError):\n",
        "        return {\"error\": f\"Could not fetch financials for symbol: {symbol}\"}\n",
        "\n",
        "def get_income_statement(symbol):\n",
        "    \"\"\"\n",
        "    Fetch last income statement for the given company symbol such as revenue, gross profit, net income, EBITDA, EPS.\n",
        "    \"\"\"\n",
        "    url = f\"https://financialmodelingprep.com/api/v3/income-statement/{symbol}?period=annual&apikey={FINANCIAL_MODELING_PREP_API_KEY}\"\n",
        "    response = requests.get(url)\n",
        "    data = response.json()\n",
        "    try:\n",
        "        results = data[0]\n",
        "        financials = {\n",
        "            \"date\": results[\"date\"],\n",
        "            \"revenue\": results[\"revenue\"],\n",
        "            \"gross profit\": results[\"grossProfit\"],\n",
        "            \"net Income\": results[\"netIncome\"],\n",
        "            \"ebitda\": results[\"ebitda\"],\n",
        "            \"EPS\": results[\"eps\"],\n",
        "            \"EPS diluted\":results[\"epsdiluted\"]\n",
        "        }\n",
        "        return data, financials\n",
        "    except (IndexError, KeyError):\n",
        "        return {\"error\": f\"Could not fetch financials for symbol: {symbol}\"}"
      ],
      "metadata": {
        "id": "GLwWW7scMivB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Specify the Agents"
      ],
      "metadata": {
        "id": "JUeJTgxdbOUD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-  **manager_agent**: This agent will handle determning and switching between agents to transfer the user's request to the best suited agent.\n",
        "\n",
        "-  **stock_price_agent**: This agent is responsible of fetching the last available price, volume, average price 50d, 200d, EPS, PE, and the next earnings annoucement.\n",
        "\n",
        "-  **company_basic_information_agent**: This agent is responsible of collecting the company basic data like description market capitalization, sector, industry...\n",
        "\n",
        "-  **income_statement_agent**: This agent is responsible of gathering the last income statement data such as revenue, gross profit, net income, EBITDA, EPS.\n",
        "\n",
        "* For each sub-agent, I define a set of function to handle collecting the adequate data.\n",
        "* The manager agent will handle function that transfer the request to the different agents.\n",
        "* Each sub-agent will have a function that the transfer back tho the manager."
      ],
      "metadata": {
        "id": "u0GXwU-xbRqT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from swarm import Agent\n",
        "\n",
        "manager_agent = Agent(\n",
        "    name=\"Manager Agent\",\n",
        "    instructions=\"Determine which agent is best suited to handle the user's request, and transfer the conversation to that agent.\",\n",
        ")\n",
        "stock_price_agent = Agent(\n",
        "    name=\"Stock Price Agent\",\n",
        "    instructions=\"Fetch Hsitorical prices for a given stock symbol, the current volume, the average price 50d and 200d, EPS, PE and the next earnings Announcement.\",\n",
        "    functions=[get_stock_price],\n",
        ")\n",
        "\n",
        "company_basic_information_agent = Agent(\n",
        "    name=\"Company basic information Agent\",\n",
        "    instructions=\"Fetch basic financial information for the given company symbol such as the industry, the sector, the name of the company, and the market capitalization.\",\n",
        "    functions=[get_company_financials],\n",
        ")\n",
        "\n",
        "income_statement_agent = Agent(\n",
        "    name=\"Income Statement Agent\",\n",
        "    instructions=\" Fetch last income statement for the given company symbol such as revenue, gross profit, net income, EBITDA, EPS.\",\n",
        "    functions=[get_income_statement],\n",
        ")\n",
        "\n",
        "\n",
        "def transfer_back_to_manager():\n",
        "    \"\"\"Call this function if a user is asking about a topic that is not handled by the current agent.\"\"\"\n",
        "    return manager_agent\n",
        "\n",
        "\n",
        "def transfer_to_stock_price():\n",
        "    return stock_price_agent\n",
        "\n",
        "def transfer_to_company_basic_info():\n",
        "    return company_basic_information_agent\n",
        "\n",
        "def transfer_to_income_statement():\n",
        "    return income_statement_agent\n",
        "\n",
        "\n",
        "manager_agent.functions = [transfer_to_stock_price, transfer_to_company_basic_info, transfer_to_income_statement]\n",
        "stock_price_agent.functions.append(transfer_back_to_manager)\n",
        "company_basic_information_agent.functions.append(transfer_back_to_manager)\n",
        "income_statement_agent.functions.append(transfer_back_to_manager)"
      ],
      "metadata": {
        "id": "G2xHWJMPK7-j"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chating with the Agents:"
      ],
      "metadata": {
        "id": "YBVJT-15dLz5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I asked different questions:\n",
        "\n",
        " -  What is the current stock price of Amazon and Nvidia\n",
        "\n",
        " -  What is their market capitalization?\n",
        "\n",
        " -  What is the EPS of Apple?\n",
        "\n",
        " -  When will the next earnings announcement be?\n",
        "\n",
        " -  What is the revenue of Amazon?\n",
        "\n",
        " You can see the different calls and communications between the manager agent and the different sub-agents:"
      ],
      "metadata": {
        "id": "Pk2rDGd0oCKI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can get this code from \"from swarm.repl import run_demo_loop\"==>I've extracted here to add \"exit\" to break the loop.\n",
        "import json\n",
        "\n",
        "from swarm import Swarm\n",
        "\n",
        "def process_and_print_streaming_response(response):\n",
        "    content = \"\"\n",
        "    last_sender = \"\"\n",
        "\n",
        "    for chunk in response:\n",
        "        if \"sender\" in chunk:\n",
        "            last_sender = chunk[\"sender\"]\n",
        "\n",
        "        if \"content\" in chunk and chunk[\"content\"] is not None:\n",
        "            if not content and last_sender:\n",
        "                print(f\"\\033[94m{last_sender}:\\033[0m\", end=\" \", flush=True)\n",
        "                last_sender = \"\"\n",
        "            print(chunk[\"content\"], end=\"\", flush=True)\n",
        "            content += chunk[\"content\"]\n",
        "\n",
        "        if \"tool_calls\" in chunk and chunk[\"tool_calls\"] is not None:\n",
        "            for tool_call in chunk[\"tool_calls\"]:\n",
        "                f = tool_call[\"function\"]\n",
        "                name = f[\"name\"]\n",
        "                if not name:\n",
        "                    continue\n",
        "                print(f\"\\033[94m{last_sender}: \\033[95m{name}\\033[0m()\")\n",
        "\n",
        "        if \"delim\" in chunk and chunk[\"delim\"] == \"end\" and content:\n",
        "            print()  # End of response message\n",
        "            content = \"\"\n",
        "\n",
        "        if \"response\" in chunk:\n",
        "            return chunk[\"response\"]\n",
        "\n",
        "\n",
        "def pretty_print_messages(messages) -> None:\n",
        "    for message in messages:\n",
        "        if message[\"role\"] != \"assistant\":\n",
        "            continue\n",
        "\n",
        "        # print agent name in blue\n",
        "        print(f\"\\033[94m{message['sender']}\\033[0m:\", end=\" \")\n",
        "\n",
        "        # print response, if any\n",
        "        if message[\"content\"]:\n",
        "            print(message[\"content\"])\n",
        "\n",
        "        # print tool calls in purple, if any\n",
        "        tool_calls = message.get(\"tool_calls\") or []\n",
        "        if len(tool_calls) > 1:\n",
        "            print()\n",
        "        for tool_call in tool_calls:\n",
        "            f = tool_call[\"function\"]\n",
        "            name, args = f[\"name\"], f[\"arguments\"]\n",
        "            arg_str = json.dumps(json.loads(args)).replace(\":\", \"=\")\n",
        "            print(f\"\\033[95m{name}\\033[0m({arg_str[1:-1]})\")\n",
        "\n",
        "\n",
        "def run_demo_loop(\n",
        "    starting_agent, context_variables=None, stream=False, debug=False\n",
        ") -> None:\n",
        "    client = Swarm()\n",
        "    print(\"Starting Swarm CLI 🐝\")\n",
        "\n",
        "    messages = []\n",
        "    agent = starting_agent\n",
        "\n",
        "    while True:\n",
        "        user_input = input(\"\\033[90mUser\\033[0m: \")\n",
        "        if user_input.lower() == \"exit\":\n",
        "            break\n",
        "        messages.append({\"role\": \"user\", \"content\": user_input})\n",
        "\n",
        "        response = client.run(\n",
        "            agent=agent,\n",
        "            messages=messages,\n",
        "            context_variables=context_variables or {},\n",
        "            stream=stream,\n",
        "            debug=debug,\n",
        "        )\n",
        "\n",
        "        if stream:\n",
        "            response = process_and_print_streaming_response(response)\n",
        "        else:\n",
        "            pretty_print_messages(response.messages)\n",
        "\n",
        "        messages.extend(response.messages)\n",
        "        agent = response.agent\n"
      ],
      "metadata": {
        "id": "8Z1wkUBMVBvX"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# write \"exit\" to exit the loop\n",
        "run_demo_loop(manager_agent, debug=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mw7GSjvOaaVy",
        "outputId": "07133226-d0e1-46cd-b131-70277f9a89ab"
      },
      "execution_count": 18,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting Swarm CLI 🐝\n",
            "\u001b[90mUser\u001b[0m: What is the current stock price of Amazon and Nvidia?\n",
            "\u001b[94mManager Agent\u001b[0m: \n",
            "\u001b[95mtransfer_to_stock_price\u001b[0m()\n",
            "\u001b[95mtransfer_to_stock_price\u001b[0m()\n",
            "\u001b[94mStock Price Agent\u001b[0m: \n",
            "\u001b[95mget_stock_price\u001b[0m(\"symbol\"= \"AMZN\")\n",
            "\u001b[95mget_stock_price\u001b[0m(\"symbol\"= \"NVDA\")\n",
            "\u001b[94mStock Price Agent\u001b[0m: The current stock information is as follows:\n",
            "\n",
            "**Amazon (AMZN)**:\n",
            "- Current Price: $191.23\n",
            "- Volume: 15,906,252\n",
            "- Average Price (50 days): $182.20 \n",
            "- Average Price (200 days): $178.90\n",
            "- Earnings Per Share (EPS): $4.20\n",
            "- Price to Earnings Ratio (PE): 45.53\n",
            "- Next Earnings Announcement: October 31, 2024\n",
            "\n",
            "**Nvidia (NVDA)**:\n",
            "- Current Price: $143.91\n",
            "- Volume: 166,377,366\n",
            "- Average Price (50 days): $122.37\n",
            "- Average Price (200 days): $100.89\n",
            "- Earnings Per Share (EPS): $2.14\n",
            "- Price to Earnings Ratio (PE): 67.25\n",
            "- Next Earnings Announcement: November 21, 2024\n",
            "\u001b[90mUser\u001b[0m: What is their market capitalization?\n",
            "\u001b[94mStock Price Agent\u001b[0m: \u001b[95mtransfer_back_to_manager\u001b[0m()\n",
            "\u001b[94mManager Agent\u001b[0m: \n",
            "\u001b[95mtransfer_to_company_basic_info\u001b[0m()\n",
            "\u001b[95mtransfer_to_company_basic_info\u001b[0m()\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: \n",
            "\u001b[95mget_company_financials\u001b[0m(\"symbol\"= \"AMZN\")\n",
            "\u001b[95mget_company_financials\u001b[0m(\"symbol\"= \"NVDA\")\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: Here are the market capitalizations for the companies:\n",
            "\n",
            "- **Amazon.com, Inc. (AMZN)**\n",
            "  - Market Capitalization: $2.01 Trillion\n",
            "  - Industry: Specialty Retail\n",
            "  - Sector: Consumer Cyclical\n",
            "\n",
            "- **NVIDIA Corporation (NVDA)**\n",
            "  - Market Capitalization: $3.53 Trillion\n",
            "  - Industry: Semiconductors\n",
            "  - Sector: Technology\n",
            "\u001b[90mUser\u001b[0m: What is the EPS of Apple?\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: \u001b[95mtransfer_back_to_manager\u001b[0m()\n",
            "\u001b[94mManager Agent\u001b[0m: \u001b[95mtransfer_to_company_basic_info\u001b[0m()\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: \u001b[95mget_company_financials\u001b[0m(\"symbol\"= \"AAPL\")\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: The Earnings Per Share (EPS) information is not available in the data I retrieved for Apple Inc. (AAPL). Please check a financial news website or stock market service for the most up-to-date EPS figures.\n",
            "\u001b[90mUser\u001b[0m: check again\n",
            "\u001b[94mCompany basic information Agent\u001b[0m: \u001b[95mtransfer_back_to_manager\u001b[0m()\n",
            "\u001b[94mManager Agent\u001b[0m: \u001b[95mtransfer_to_stock_price\u001b[0m()\n",
            "\u001b[94mStock Price Agent\u001b[0m: \u001b[95mget_stock_price\u001b[0m(\"symbol\"= \"AAPL\")\n",
            "\u001b[94mStock Price Agent\u001b[0m: The Earnings Per Share (EPS) for Apple Inc. (AAPL) is $6.57.\n",
            "\u001b[90mUser\u001b[0m: When will the next earnings announcement be?\n",
            "\u001b[94mStock Price Agent\u001b[0m: The next earnings announcement for Apple Inc. (AAPL) is scheduled for October 31, 2024.\n",
            "\u001b[90mUser\u001b[0m: What is the revenue of Amazon?\n",
            "\u001b[94mStock Price Agent\u001b[0m: \u001b[95mtransfer_back_to_manager\u001b[0m()\n",
            "\u001b[94mManager Agent\u001b[0m: \u001b[95mtransfer_to_income_statement\u001b[0m()\n",
            "\u001b[94mIncome Statement Agent\u001b[0m: \u001b[95mget_income_statement\u001b[0m(\"symbol\"= \"AMZN\")\n",
            "\u001b[94mIncome Statement Agent\u001b[0m: Amazon's revenue for the fiscal year ending on December 31, 2023, was approximately $574.79 billion.\n",
            "\u001b[90mUser\u001b[0m: exit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Key Takeways:\n",
        "\n",
        "-  A highly customizable framework: Each agent is equipped with specific instructions to ensure clarity and optimal outcomes.\n",
        "\n",
        "-  The implementation is broken down to the most granular level, allowing you to build your agent exactly as you desire.\n",
        "\n",
        "-  There is no built-in memory management like the Assistant AI API provides."
      ],
      "metadata": {
        "id": "Ud_RLthAiujL"
      }
    }
  ]
}